Name
Abstract | ||
|---|---|---|
Monitoring network traffic and detecting unwanted applications has become a challenging problem, since many applications obfuscate their traffic using unregistered port numbers or payload encryption. Apart from some notable exceptions, most traffic monitoring tools use two types of approaches: (a) keeping traffic statistics such as packet sizes and interarrivals, flow counts, byte volumes, etc., or (b) analyzing packet content. In this paper, we propose the use of Traffic Dispersion Graphs (TDGs) as a way to monitor, analyze, and visualize network traffic. TDGs model the social behavior of hosts ("who talks to whom"), where the edges can be defined to represent different interactions (e.g. the exchange of a certain number or type of packets). With the introduction of TDGs, we are able to harness a wealth of tools and graph modeling techniques from a diverse set of disciplines. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1298306.1298349 | Internet Measurement Comference |
Keywords | Field | DocType |
certain number,byte volume,tdgs model,network monitoring,traffic dispersion graphs,monitoring network traffic,traffic monitoring tool,network traffic,traffic statistic,traffic dispersion graph,packet size,packet content,connected graph | Traffic generation model,Byte,Computer science,Network packet,Computer network,Encryption,Network monitoring,Obfuscation,Network traffic control,Payload,Distributed computing | Conference |
Citations | PageRank | References |
83 | 3.12 | 8 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marios Iliofotou | 1 | 476 | 18.49 |
| Prashanth Pappu | 2 | 149 | 11.01 |
| Michalis Faloutsos | 3 | 5288 | 586.88 |
| Michael Mitzenmacher | 4 | 7386 | 730.89 |
| Sumeet Singh | 5 | 932 | 55.65 |
| George Varghese | 6 | 8149 | 727.66 |